OpenRaiser/Intern-Atlas huggingface dataset:
IoD-Crypt: A Lightweight Cryptographic Framework for Internet of Drones
Venue: arXiv cs.CR 2019
Year: 2019

Internet of Drones (IoD) is expected to play a central role in many civilian and military applications, that require sensitive and mission-critical information to be processed. It is therefore vital to ensure the security and privacy of IoD. However, unlike traditional networks, IoD has a broader attack surface and is highly energy-constrained, which hinder the direct adoption of standard cryptographic protocols for IoD. We propose an energy-efficient cryptographic framework (namely IoD-Crypt), which can potentially meet the requirements of battery-limited IoD. Specifically, IoD-Crypt utilizes special precomputation techniques and self-certified primitives to gain significant computation and communication efficiency over the standard public key cryptography (PKC) suites. Our integrations and optimizations are broadly applicable to key exchange, digital signature and public key encryption schemes that encompass generic applications of PKC in IoD. We prove that IoD-Crypt is secure in the random oracle model. We fully implemented IoD-Crypt on two common drone processors, namely 8-bit AVR and 32-bit ARM, and conducted an in-depth energy analysis. Our experiments (on both platforms) showed that IoD-Crypt offers up to 48x less energy consumption compared to standard techniques. We have open-sourced our implementations for wide adoption and public testing purposes.
On the Minimum-Area Rectangular and Square Annulus Problem
Venue: arXiv cs.CG 2019
Year: 2019

In this paper, we address the minimum-area rectangular and square annulus problem, which asks a rectangular or square annulus of minimum area, either in a fixed orientation or over all orientations, that encloses a set $P$ of $n$ input points in the plane. To our best knowledge, no nontrivial results on the problem have been discussed in the literature, while its minimum-width variants have been intensively studied. For a fixed orientation, we show reductions to well-studied problems: the minimum-width square annulus problem and the largest empty rectangle problem, yielding algorithms of time complexity $O(n\log^2 n)$ and $O(n\log n)$ for the rectangular and square cases, respectively. In arbitrary orientation, we present $O(n^3)$-time algorithms for the rectangular and square annulus problem by enumerating all maximal empty rectangles over all orientations. The same approach is shown to apply also to the minimum-width square annulus problem and the largest empty square problem over all orientations, resulting in $O(n^3)$-time algorithms for both problems. Consequently, we improve the previously best algorithm for the minimum-width square annulus problem by a factor of logarithm, and present the first algorithm for the largest empty square problem in arbitrary orientation. We also consider bicriteria optimization variants, computing a minimum-width minimum-area or minimum-area minimum-width annulus.
Pedestrian Detection in Thermal Images using Saliency Maps
Venue: arXiv cs.CV 2019
Year: 2019

Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art techniques employ a fusion network that uses features from paired thermal and color images. Instead, we propose to augment thermal images with their saliency maps, to serve as an attention mechanism for the pedestrian detector especially during daytime. We investigate how such an approach results in improved performance for pedestrian detection using only thermal images, eliminating the need for paired color images. For our experiments, we train the Faster R-CNN for pedestrian detection and report the added effect of saliency maps generated using static and deep methods (PiCA-Net and R3-Net). Our best performing model results in an absolute reduction of miss rate by 13.4% and 19.4% over the baseline in day and night images respectively. We also annotate and release pixel level masks of pedestrians on a subset of the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly available dataset for salient pedestrian detection.
How to Price Fresh Data
Venue: arXiv cs.NI 2019
Year: 2019

We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the {\it age of information} (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.
Learning Deformable Kernels for Image and Video Denoising
Venue: arXiv cs.CV 2019
Year: 2019

Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input. Instead of relying on hand-crafted selecting and averaging strategies, we propose to explicitly learn this process with deep neural networks. Specifically, we propose deformable 2D kernels for image denoising where the sampling locations and kernel weights are both learned. The proposed kernel naturally adapts to image structures and could effectively reduce the oversmoothing artifacts. Furthermore, we develop 3D deformable kernels for video denoising to more efficiently sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues of large motion from dynamic scenes. For better training our video denoising model, we introduce the trilinear sampler and a new regularization term. We demonstrate that the proposed method performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.
Learning to Navigate in Indoor Environments: from Memorizing to Reasoning
Venue: arXiv cs.RO 2019
Year: 2019

Autonomous navigation is an essential capability of smart mobility for mobile robots. Traditional methods must have the environment map to plan a collision-free path in workspace. Deep reinforcement learning (DRL) is a promising technique to realize the autonomous navigation task without a map, with which deep neural network can fit the mapping from observation to reasonable action through explorations. It should not only memorize the trained target, but more importantly, the planner can reason out the unseen goal. We proposed a new motion planner based on deep reinforcement learning that can arrive at new targets that have not been trained before in the indoor environment with RGB image and odometry only. The model has a structure of stacked Long Short-Term memory (LSTM). Finally, experiments were implemented in both simulated and real environments. The source code is available: https://github.com/marooncn/navbot.
On the Performance of Differential Evolution for Hyperparameter Tuning
Venue: arXiv cs.LG 2019
Year: 2019

Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evolution for tuning hyperparameters of supervised learning algorithms for classification tasks. This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach. The results indicate that Differential Evolution outperforms SMAC for most datasets when tuning a given machine learning algorithm - particularly when breaking ties in a first-to-report fashion. Only for the tightest of computational budgets SMAC performs better. On small datasets, Differential Evolution outperforms SMAC by 19% (37% after tie-breaking). In a second experiment across a range of representative datasets taken from the literature, Differential Evolution scores 15% (23% after tie-breaking) more wins than SMAC.
Segmenting Potentially Cancerous Areas in Prostate Biopsies using Semi-Automatically Annotated Data
Venue: arXiv cs.CV 2019
Year: 2019

Gleason grading specified in ISUP 2014 is the clinical standard in staging prostate cancer and the most important part of the treatment decision. However, the grading is subjective and suffers from high intra and inter-user variability. To improve the consistency and objectivity in the grading, we introduced glandular tissue WithOut Basal cells (WOB) as the ground truth. The presence of basal cells is the most accepted biomarker for benign glandular tissue and the absence of basal cells is a strong indicator of acinar prostatic adenocarcinoma, the most common form of prostate cancer. Glandular tissue can objectively be assessed as WOB or not WOB by using specific immunostaining for glandular tissue (Cytokeratin 8/18) and for basal cells (Cytokeratin 5/6 + p63). Even more, WOB allowed us to develop a semi-automated data generation pipeline to speed up the tremendously time consuming and expensive process of annotating whole slide images by pathologists. We generated 295 prostatectomy images exhaustively annotated with WOB. Then we used our Deep Learning Framework, which achieved the $2^{nd}$ best reported score in Camelyon17 Challenge, to train networks for segmenting WOB in needle biopsies. Evaluation of the model on 63 needle biopsies showed promising results which were improved further by finetuning the model on 118 biopsies annotated with WOB, achieving F1-score of 0.80 and Precision-Recall AUC of 0.89 at the pixel-level. Then we compared the performance of the model against 17 biopsies annotated independently by 3 pathologists using only H\&E staining. The comparison demonstrated that the model performed on a par with the pathologists. Finally, the model detected and accurately outlined existing WOB areas in two biopsies incorrectly annotated as totally WOB-free biopsies by three pathologists and in one biopsy by two pathologists.
The Proceedings of First Work-in-Progress Session of The CSI International Symposium on Real-Time and Embedded Systems and Technologies
Venue: arXiv cs.DC 2019
Year: 2019

The present volume contains the proceedings of RTEST WiP 2018, chaired by Marco Caccamo, University of Illinois at Urbana-Champaign. This event has been organized by the School of Electrical and Computer Engineering at the University of Tehran, in conjunction with the Department of Computer Engineering at Sharif University of Technology, Tehran, Iran. The topics of interest in RTEST WiP span over all theoretical and application-oriented aspects, reporting design, analysis, implementation, evaluation, and empirical results, of real-time and embedded systems, internet-of-things, and cyber-physical systems. The program committee of RTEST 2018 consists of 54 top researchers in the mentioned fields from top universities, industries, and research centers around the world. RTEST 2018 has received a total of 41 submissions, out of which we have accepted 14 regular papers and 4 work-in-progress papers. Each submission has been reviewed by 3 to 5 independent referees, for its quality, originality, contribution, clarity of presentation, and relevance to the symposium topics.
SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning
Venue: arXiv cs.CV 2019
Year: 2019

The existing Zero-Shot learning (ZSL) methods may suffer from the vague class attributes that are highly overlapped for different classes. Unlike these methods that ignore the discrimination among classes, in this paper, we propose to classify unseen image by rectifying the semantic space guided by the visual space. First, we pre-train a Semantic Rectifying Network (SRN) to rectify semantic space with a semantic loss and a rectifying loss. Then, a Semantic Rectifying Generative Adversarial Network (SR-GAN) is built to generate plausible visual feature of unseen class from both semantic feature and rectified semantic feature. To guarantee the effectiveness of rectified semantic features and synthetic visual features, a pre-reconstruction and a post reconstruction networks are proposed, which keep the consistency between visual feature and semantic feature. Experimental results demonstrate that our approach significantly outperforms the state-of-the-arts on four benchmark datasets.
Management of mobile resources in Physical Internet logistic models
Venue: arXiv cs.DC 2019
Year: 2019

This paper deals with the concept of a 'Physical Internet', the idea of building large logistics systems like the very successful Digital Internet network. The idea is to handle mobile resources, such as containers, just like Internet data packets. Thus, it is possible to use the principles of encapsulation and routing to optimize the freight. The problem is that mobile resources, such as containers, are not quite similar to data packets, because they are real and not dematerialized. Thus the handling and the storing of mobile resources, such as containers, will create imbalances in the logistics network, leading to starvation or overstocking of logistic network nodes. We propose in this paper a study addressing this problem leading to some solutions.
Learning the undecidable from networked systems
Venue: arXiv cs.DC 2019
Year: 2019

This article presents a theoretical investigation of computation beyond the Turing barrier from emergent behavior in distributed systems. In particular, we present an algorithmic network that is a mathematical model of a networked population of randomly generated computable systems with a fixed communication protocol. Then, in order to solve an undecidable problem, we study how nodes (i.e., Turing machines or computable systems) can harness the power of the metabiological selection and the power of information sharing (i.e., communication) through the network. Formally, we show that there is a pervasive network topological condition, in particular, the small-diameter phenomenon, that ensures that every node becomes capable of solving the halting problem for every program with a length upper bounded by a logarithmic order of the population size. In addition, we show that this result implies the existence of a central node capable of emergently solving the halting problem in the minimum number of communication rounds. Furthermore, we introduce an algorithmic-informational measure of synergy for networked computable systems, which we call local algorithmic synergy. Then, we show that such algorithmic network can produce an arbitrarily large value of expected local algorithmic synergy.
Sound, Fine-Grained Traversal Fusion for Heterogeneous Trees - Extended Version
Venue: arXiv cs.PL 2019
Year: 2019

Applications in many domains are based on a series of traversals of tree structures, and fusing these traversals together to reduce the total number of passes over the tree is a common, important optimization technique. In applications such as compilers and render trees, these trees are heterogeneous: different nodes of the tree have different types. Unfortunately, prior work for fusing traversals falls short in different ways: they do not handle heterogeneity; they require using domain-specific languages to express an application; they rely on the programmer to aver that fusing traversals is safe, without any soundness guarantee; or they can only perform coarse-grain fusion, leading to missed fusion opportunities. This paper addresses these shortcomings to build a framework for fusing traversals of heterogeneous trees that is automatic, sound, and fine-grained. We show across several case studies that our approach is able to allow programmers to write simple, intuitive traversals, and then automatically fuse them to substantially improve performance.
Repeat-Authenticate Scheme for Multicasting of Blockchain Information in IoT Systems
Venue: arXiv cs.DC 2019
Year: 2019

We study the problem of efficiently disseminating authenticated blockchain information from blockchain nodes (servers) to Internet of Things (IoT) devices, through a wireless base station (BS). In existing blockchain protocols, upon generation of a new block, each IoT device receives a copy of the block header, authenticated via digital signature by one or more trusted servers. Since it relies on unicast transmissions, the required communication resources grow linearly with the number of IoT devices. We propose a more efficient scheme, in which a single copy of each block header is multicasted, together with the signatures of servers. In addition, if IoT devices tolerate a delay, we exploit the blockchain structure to amortize the authentication in time, by transmitting only a subset of signature in each block period. Finally, the BS sends redundant information, via a repetition code, to deal with the unreliable wireless channel, with the aim of decreasing the amount of feedback required from IoT devices. Our analysis shows the trade-off between timely authentication of blocks and reliability of the communication, depending on the packet loss rate offered by the channel. The numerical results show that the performance benefits of the proposed scheme makes it a viable starting point for designing new lightweight protocols for blockchains.
The Native AQM for L4S Traffic
Venue: arXiv cs.NI 2019
Year: 2019

This memo focuses solely on the native AQM of Low Latency Low Loss Scalable throughput (L4S) traffic and proposes various improvements to the original step design. One motivation for DCTCP to use simple step marking was that it was possible to deploy it by merely configuring the RED implementations in existing hardware, albeit in an unexpected configuration by setting parameters to degenerate values. However, there is no longer any imperative to stick with the original DCTCP step-function design, because changes will be needed to implement the DualQ Coupled AQM anyway, and the requirements for L4S congestion controls are not yet set in stone either. This paper proposes gradient (ramp) marking and a virtual (a.k.a. phantom) queue. It provides a way to implement virtual queuing delay (a.k.a. virtual sojourn time) and scaled virtual sojourn time.
Processsing Simple Geometric Attributes with Autoencoders
Venue: arXiv cs.CV 2019
Year: 2019

Image synthesis is a core problem in modern deep learning, and many recent architectures such as autoencoders and Generative Adversarial networks produce spectacular results on highly complex data, such as images of faces or landscapes. While these results open up a wide range of new, advanced synthesis applications, there is also a severe lack of theoretical understanding of how these networks work. This results in a wide range of practical problems, such as difficulties in training, the tendency to sample images with little or no variability, and generalisation problems. In this paper, we propose to analyse the ability of the simplest generative network, the autoencoder, to encode and decode two simple geometric attributes : size and position. We believe that, in order to understand more complicated tasks, it is necessary to first understand how these networks process simple attributes. For the first property, we analyse the case of images of centred disks with variable radii. We explain how the autoencoder projects these images to and from a latent space of smallest possible dimension, a scalar. In particular, we describe a closed-form solution to the decoding training problem in a network without biases, and show that during training, the network indeed finds this solution. We then investigate the best regularisation approaches which yield networks that generalise well. For the second property, position, we look at the encoding and decoding of Dirac delta functions, also known as `one-hot' vectors. We describe a hand-crafted filter that achieves encoding perfectly, and show that the network naturally finds this filter during training. We also show experimentally that the decoding can be achieved if the dataset is sampled in an appropriate manner.
Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks
Venue: arXiv cs.CR 2019
Year: 2019

Temperature sensing and control systems are widely used in the closed-loop control of critical processes such as maintaining the thermal stability of patients, or in alarm systems for detecting temperature-related hazards. However, the security of these systems has yet to be completely explored, leaving potential attack surfaces that can be exploited to take control over critical systems. In this paper we investigate the reliability of temperature-based control systems from a security and safety perspective. We show how unexpected consequences and safety risks can be induced by physical-level attacks on analog temperature sensing components. For instance, we demonstrate that an adversary could remotely manipulate the temperature sensor measurements of an infant incubator to cause potential safety issues, without tampering with the victim system or triggering automatic temperature alarms. This attack exploits the unintended rectification effect that can be induced in operational and instrumentation amplifiers to control the sensor output, tricking the internal control loop of the victim system to heat up or cool down. Furthermore, we show how the exploit of this hardware-level vulnerability could affect different classes of analog sensors that share similar signal conditioning processes. Our experimental results indicate that conventional defenses commonly deployed in these systems are not sufficient to mitigate the threat, so we propose a prototype design of a low-cost anomaly detector for critical applications to ensure the integrity of temperature sensor signals.
The Architectural Dynamics of Encapsulated Botnet Detection (EDM)
Venue: arXiv cs.CR 2019
Year: 2019

Botnet is one of the numerous attacks ravaging the networking environment. Its approach is said to be brutal and dangerous to network infrastructures as well as client systems. Since the introduction of botnet, different design methods have been employed to solve the divergent approach but the method of taking over servers and client systems is unabated. To solve this, we first identify Mpack, ICEpack and Fiesta as enhanced IRC tool. The analysis of its role in data exchange using OSI model was carried out. This further gave the needed proposal to the development of a High level architecture representing the structural mechanism and the defensive mechanism within network server so as to control the botnet trend. Finally, the architecture was designed to respond in a proactive state when scanning and synergizing the double data verification modules in an encapsulation manner within server system.